Leveraging computer vision and machine learning to identify compelling scenes

ABSTRACT

Methods and apparatus are described for generating compelling preview clips of media presentations. Compelling clips are identified based on the extent to which human faces are shown and/or the loudness of the audio associated with the clips. One or more of these compelling clips are then provided to a client device for playback.

INCORPORATION BY REFERENCE

An Application Data Sheet is filed concurrently with this specificationas part of the present application. Each application that the presentapplication claims benefit of or priority to as identified in theconcurrently filed Application Data Sheet is incorporated by referenceherein in its entirety and for all purposes.

BACKGROUND

Users have an ever-increasing array of options for consuming mediapresentation, in terms of the types of media presentation (e.g., video,audio, etc.), providers of the media presentation, and devices forconsuming the media presentation. Media presentation providers arebecoming increasingly sophisticated and effective at providing mediapresentation quickly and reliably to users.

Given the wide variety of available content, users may find it difficultto decide what to watch. Providing a short but compelling preview ofmedia presentation may help users make an informed decision about whatto watch. Unfortunately, the content in a preview may not be tailored toa specific customer's preferences and so may not adequately support suchdecision making. Furthermore, it is difficult to identify compellingclips of media presentation without time-intensive manual review andlabeling.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of a particular class of implementationsfor identifying and selecting compelling preview clips.

FIG. 2 illustrates an example of a computing environment in whichimplementations enabled by the present disclosure may be practiced.

FIGS. 3 presents a flowchart illustrating operations of examples ofimplementations as described herein.

FIGS. 4, 5, and 6 present flowcharts illustrating operations forselecting compelling clips as described herein.

DETAILED DESCRIPTION

This disclosure describes techniques for identifying clips of compellingscenes associated with a media presentation and providing such clips(e.g., as previews) based on user preferences or other characteristics.A “clip” is a sequence of successive frames of a media presentation thatbegin or end on a shot boundary. A “shot” is a sequence of successiveframes of the media presentation that are visually similar (e.g., framesbelonging to the same scene). Clips corresponding to compelling scenesare initially identified based on the extent to which the faces ofcharacters are shown and/or the loudness of the clips. Character-themedclips featuring close-up views of the main characters can attract auser's attention, while louder clips often correspond to “highintensity” scenes that may also be engaging for users. As will bedescribed, scene boundaries are also identified to reduce the likelihoodthat the identified clips don't start or end in the middle of a shot. Asubset of the identified clips is then selected based on metrics thatcorrelate with compelling content.

The selected clips are then used to provide personalized sequences as,for example, previews in an interface used by a viewer for mediapresentation selection. These personalized previews are intended to helpusers discover content they may be inclined to watch. For example, usersmay be provided with clips featuring characters or performers theyprefer. Alternatively, or in addition, users may be provided with clipsfeaturing “high intensity” scenes based on a loudness of the clip.

FIG. 1 illustrates an example of providing personalized clips of a mediapresentation as enabled by the present disclosure. Frames 108 representportions of a media presentation, which might be a movie or an episodeof a show. For illustrative purposes, FIG. 1 shows the mediapresentation as including one frame per second. However, those of skillin the art will understand that this is being done for purposes ofexposition, and that media content typically includes many more framesper second, e.g., about 30 or 60 frames per second. Graph 110 provides agraphical representation of the loudness of the audio associated withframes having time stamps between 1:23:00 and 1:24:59.

To identify compelling clips, clips 105 a-c are initially identifiedfrom frames 108. The time stamps for clips 105 a-c are illustrated vialines markers on frames 108 and via boxes on graph 110. Clip 105 aextends from 1:23:15 to 1:23:57, clip 105 b extends from 1:23:45 to1:24:20, and clip 105 c extends from 1:24:15 to 1:24:57. The manner inwhich these clips are identified is discussed in greater detail below.While only 3 clips are shown in FIG. 1 , it should be understood that,typically, many more clips are identified across the entire mediapresentation. To identify compelling clips based on faces of characters,each of clips 105 a-c is analyzed to determine the extent to which thefaces of characters are shown and subsequently ranked. As illustrated inframes 108, frames of a clip 105 c (extending from 1:24:15 to 1:24:57)have larger faces than the frames of clips 105 a and 105 b. Because clip105 c includes faces to a greater extent than clip 105 a or clip 105 bit receives a higher ranking. Based on that higher ranking, clip 105 cmay be identified as a compelling clip for the media presentation.

Another process may be employed to analyze and select clips based onloudness. For example, graph 110 represents a measure of the loudness ofthe media presentation over time. A loudness threshold 109 is used toidentify clips that exceed the loudness threshold throughout the clip.As illustrated in graph 110, clip 107 is louder than loudness threshold109 throughout the duration of clip 107. Thus, clip 107 may beidentified as a compelling clip for the media presentation based onloudness.

Both types of compelling clips are then provided to a personalizationengine. When a user is browsing a media service to find a mediapresentation to watch, the personalization engine selects one or more ofthe compelling clips based on preferences and/or characteristics of theuser or user device and features of the compelling clips. The selectedclip(s) are then provided to the user device for playback, e.g., as apreview.

FIG. 2 illustrates an example of a computing environment in whichpersonalized preview clips may be generated and presented as enabled bythe present disclosure. The computing environment of FIG. 2 includesmedia server 210 which can be used to provide a media presentation forplayback on devices 205 a-e.

It should be noted that, despite references to particular computingparadigms and software tools herein, the computer program instructionson which various implementations are based may correspond to any of awide variety of programming languages, software tools and data formats,may be stored in any type of non-transitory computer-readable storagemedia or memory device(s), and may be executed according to a variety ofcomputing models including, for example, a client/server model, apeer-to-peer model, on a stand-alone computing device, or according to adistributed computing model in which various functionalities may beeffected or employed at different locations. In addition, reference toparticular types of media presentations herein is merely by way ofexample. Suitable alternatives known to those of skill in the art may beemployed.

Media server 210 may be part of a content delivery system that conformsto any of a wide variety of architectures. The functionality andcomponents of media server 210 can use one or more servers and bedeployed at one or more geographic locations (e.g., across differentcountries, states, cities, etc.) using a network such as any subset orcombination of a wide variety of network environments including, forexample, TCP/IP-based networks, telecommunications networks, wirelessnetworks, cable networks, public networks, private networks, wide areanetworks, local area networks, the Internet, the World Wide Web,intranets, extranets, etc.

Media server 210 can include various types of logic used to providemedia presentations for playback at devices 205 a-e. In FIG. 2 , mediaserver 210 includes media presentation storage 225, frame information220, and preview clip information 235. Media server 210 also includesplayback handling logic 230, preview clip identification logic 240,preview clip curation logic 245, and preview clip personalization logic255.

Media presentation storage 225 stores a variety of media presentationsfor playback on devices 205 a-e, such as episodes of television shows,movies, music, etc. Preview clip information 235 can be a storagemechanism, such as a database, storing metadata relating to previewclips corresponding to subsets of frames (i.e., still images in video)of television shows. For example, preview clips of every episode of atelevision show stored in media presentation storage 225 can berepresented by an episode identifier, a start time, and an end timestored in preview clip information 235. In some implementations, eachpreview clip in preview clip information 235 may be associated withadditional information, such as, for example, an importance ranking,relationship to other preview clips, relationship to a correspondingscene, a category (e.g., action, romance, etc.), a setting, includedactors, closed-captioned data providing a transcript of the dialogue inthe preview clip, script data providing production details, a plot arcidentifier, user rankings or commentary, trivia, and so forth.

In certain implementations, at least some of the contents of previewclip information 235 may be generated automatically. For example, imageprocessing of video frames to identify shot boundaries, changes inscenery, and/or characters depicted, audio processing to determineloudness and detect changes in music, ambient audio, and dialogue, andnatural language processing of textual data in the script or availablesubtitles to perform content and sentiment analysis, may each contributeto both selecting preview clips for a media presentation and associatingmetadata with the preview clips.

It should be appreciated that the techniques for generating personalizedpreview clip sequences as described herein are compatible with a widevariety of time frames for the generation of preview clip information235. For example, for a television show having multiple seasons andepisodes, personalized preview clip sequences can use preview clipinformation 235 that may have been generated months in advance, such asat the time of production of the television show episode.

Media server 210 also can include one or more processors 215, memory,and other hardware for performing the tasks disclosed herein. Forexample, playback handling logic 230 determines whether a request for apreview clip of a media presentation has been received from a viewerdevice, identifies the media presentation being requested, and retrievesuser preferences associated with the viewer device. Playback handlinglogic 230 also performs tasks relating to generating and providingmanifest data representing preview clips.

Preview clip identification logic 240 performs tasks relating toidentifying clips from media presentations, identifying shot boundaries,ranking or selecting clips according to various metrics, and selectingclips to be used for personalized previews. Preview clip curation logic245 performs tasks relating to reviewing selected clips for qualitycontrol purposes.

In some implementations, preview clip information 235, which storesmetadata relating to preview clips, can also be used to store thepreview clips themselves. For example, each preview clip may be storedas a data structure including fields corresponding to preview clipidentifiers, start and stop time modifiers, and, in someimplementations, viewer device or account identifiers.

Preview clip identification logic 240 and/or preview clip curation logic245 can interface to preview clip personalization logic 255. Previewclip personalization logic 255 performs tasks related to analysis ofpreview clips and/or selecting preview clips to provide to a user. Forexample, in certain implementations, the selection of preview clips maybe based on the relationship between the current playback history of aviewer device and a change in playback history for other viewer devices.For instance, a media server can lookup information relating to previewclips previously generated for other viewer devices. The lookup processcan return a set of preview clips that were previously generated for asimilar configuration of media presentation and device attributes. Eachclip in the set may also have an associated field indicating whether theviewer device for which the clip was provided eventually watched themedia presentation. Alternatively, the media server can retrieve thecurrent playback history for viewer devices corresponding to the set ofclips to determine whether the media presentation was eventuallywatched.

The preview clip(s) provided to any one of devices 205 a-e can be basedon the clip in the set of clips associated with the highest probabilityof a viewer device eventually watching all episodes of the mediapresentation.

In some implementations, such analysis of different preview clipsequences are used as inputs to a machine learning algorithm, such as,for example, neural networks, for modifying the criteria foridentification, curation, and/or personalization of preview clips tomaximize a particular objective, such as completion of the entire mediapresentation. In some implementations, preview clip personalizationlogic 255 may include a recommender that uses preview clip metadata,user preferences, and device information to rank and/or select a previewclip to provide to a user.

A specific implementation in which one or more previews are generatedfor a media presentation will now be described with reference to thecomputing environment of FIG. 2 and the flow diagrams of FIGS. 3, 4, and5 . Starting in step 300, shot boundaries for the media presentation areidentified. As mentioned above, a shot is a sequence of video framesdepicting visually similar content. Shot boundaries correspond to timestamps within a media presentation at which adjacent frames are visuallydistinct, e.g., representing a cut to a different shot. Consecutiveframes within a shot typically share many visual features, whileconsecutive frames across two different shots are often dramaticallydifferent. A shot boundary may be inferred based on this differencebetween two consecutive frames. The entire media presentation may beprocessed to determine such shot boundaries. For example, a mediapresentation that is about 45 minutes long might have about severalhundred shots, with corresponding shot boundaries between each shot.

In step 310 a plurality of clips may be identified based on the shotboundaries. Identifying clips based on shot boundaries is advantageousbecause starting or stopping a clip in the middle of a shot has a higherchance of the clip starting or ending in the middle of dialogue or anaction, which can create a jarring and unpleasant experience for a user.Clips that start or end on a shot boundary are more likely to includecomplete dialogue or actions, which is more compelling for a viewerdeciding whether to watch a media presentation.

In some implementations, the clips have a predetermined nominal length,e.g., about 30 seconds, about 45 seconds, about 60 seconds, about 90seconds, or about 120 seconds. In some implementations, the clips maystart or end on a shot boundary. In some implementations, the clips maystart and end on a shot boundary. Particularly in the case of the lattertype of implementation, it is typically not possible to have clips ofuniform length while still starting and ending on a shot boundary. Insuch implementations, the start of a clip may be selected based on ashot boundary, and the end of the clip is selected to be the shotboundary closest to the desired nominal length. For example, if thedesired clip length is 45 seconds, the start of the clip may be at oneshot boundary, and the end of the clip being selected to be a later shotboundary closest to 45 seconds. Thus, if there are shot boundaries 44seconds and 47 seconds after the first shot boundary, the clip may beconstrained to 44 seconds so that it ends at the corresponding shotboundary. In some implementations, there may be additional rulesregarding clips having a minimum or maximum length, such that theselected shot boundary may not be the closest shot boundary (forexample, if the clip must have a minimum length of 45 seconds, in theabove example the clip would instead end at the shot boundary 47 secondsafter the first shot boundary).

In some implementations the clips may start from each shot boundary in amedia presentation. In such implementations the clips may overlap suchthat a particular shot is included in multiple clips. While the averageduration of a shot typically varies based on film editing techniques, anaverage shot duration will often be shorter than the desired cliplength. Thus, identifying clips based on shot boundaries may result inoverlapping clips.

In some implementations, clips may start from the end of the prior clip.In such implementations the clips would not overlap. In someimplementations the clips may be identified such that there is a minimumseparation between clips, e.g., between about 30-50 shots between clips.Identifying clips with a minimum separation of shots may be used toensure that any clip that may be later selected as a compelling clipwill be diverse from any other clip that could be selected.

In step 320 a subset of the clips is selected based on metrics thatcorrespond with compelling scenes. Clips corresponding to compellingscenes may be identified based on the extent to which faces ofcharacters are shown and/or the loudness of the clip. Methods forselecting a subset of clips according to these criteria are described inreference to FIGS. 4 and 5 . In some implementations, compelling clipsmay also be identified based on loudness as described in reference toFIG. 6 .

In step 330 the selected clips are reviewed. In some implementations,review may include human operators reviewing the selected clips, whilein other implementations the review may be automated. Selected clips maybe reviewed to ensure, for example, that the clip has a minimum length,or that the clip is of sufficient quality, e.g., it has no flickering,blurriness, lip-sync errors, graininess, or watermarks. The clips mayalso be reviewed for having a natural beginning and ending, e.g., notstarting or stopping in the middle of someone speaking. In someimplementations the selected clips may also be reviewed for graphiccontent or cultural sensitivities, such as violent or sexual content. Insome implementations step 330 may also include associating variousmetadata with the selected clips. For example, the clips may be taggedwith which actors/actresses are in the clip, or that the clip is acharacter-themed clip. Such metadata may include actor profiles,critical commentary, user feedback, narrative descriptions, trivia,importance scores, association factor with a particular plot arc, andother information related to portions of the media presentation, such asa scene within an episode.

In step 340 one or more of the selected clips is provided to a clientdevice for playback. When a client device is browsing options for mediaplayback, using for example a service like Amazon Prime Video®, theclient device may send requests to a media server for preview clips ofthe various media presentations available. Personalization logicemploying a personalization model may receive the request, along withuser preferences or other characteristics associated with the userand/or client device, to determine which of the selected clips toprovide. User preferences may include celebrities, themes, moods, andgenres. In some implementations the personalization model may receivecontextual information associated with the client device, for exampledevice type, placement, time of day, media player, etc.

The personalization model may be a recommender that determines, based onthe information received from the client device and metadata associatedwith the selected clips, to provide one or more of the selected clips.For example, if user preferences associated with the device indicate theuser prefers intense action scenes over dialogue, then a clip identifiedand selected based on loudness may be provided instead of or in additionto a character-themed clip identified and selected based on average facearea.

In some implementations, the clip provided to the client device mightalso be based on a playback history associated with the client device.For example, if a user has watched the first three episodes of a series,the clip provided to the client device may be from episode 4 to providethe most relevant preview.

In some implementations, the personalization model may receive feedbackto improve its recommendations. For example, the percent of users thatselected a media presentation for playback after viewing a clip may beused to determine which of the selected clips is the better for aparticular user.

FIGS. 4 and 5 present flow diagrams for selecting a subset of clips thatare compelling as described in step 320 above. FIG. 4 provides aflowchart for selecting clips based on the extent to which human facesare represented in the clip. Clips with a large average face area may bedesirable, as these clips are more likely to feature close-up views ofmain characters that attract a user's attention. This may beadvantageous over relying on face area alone, as a clip may feature alarge number of faces, and thus have a large face area. However, in sucha crowded scene most of the faces will not correspond to maincharacters, and even if any of the main characters are included in thescene, they will not be prominently represented. Selecting clips basedsolely on the number of faces depicted may be similarly flawed. As usersare more likely to select a show based on its main characters ratherthan other characters, average face area presents a useful tool toautomatically identify clips that feature characters in which a user ismore likely to be interested.

In step 410, clips of a media presentation are identified. In someimplementations, the clips are identified based on shot boundaries asdiscussed in reference to FIG. 3 . In some implementations, the clipsmay have been previously identified.

In step 420 the representation of human faces in the content isdetermined for each clip. This may be determined by analyzing individualframes within each clip to determine the total number of faces and somemeasure of the total face area within each frame. In someimplementations, the total face area for a given frame may be determinedby bounding a rectangle around each detected face and measuring the areaof the rectangle(s). The total face area for a frame may be divided bythe total number of faces in the frame to determine an average face areafor the frame. The average face area for a clip may then be determinedbased on the average face area for frames within the clip.

In some implementations every frame in the clip may be analyzed, whilein other embodiments fewer than all of the frames of the clip may beanalyzed, e.g., every other frame, every ten frames, one frame persecond, etc. In some implementations, only I-frames are analyzed.I-frames may be decoded without reference to any other frame. Thus,analyzing only I-frames would allow for analysis of less than all frameswithout having to decode any frames that would not be analyzed. Inimplementations where less than all frames are analyzed, the averageface area for a clip may be adjusted to compensate for the unanalyzedframes, e.g., by weighting the average face area for the clip orinterpolating the average face area for unanalyzed frames.

Because the extent to which human faces are represented may bedetermined on a frame-by-frame basis, in some implementations, theaverage face area may be determined for individual frames of a mediapresentation prior to identifying clips. Regardless of when thesemeasures of face area are determined or how many frames are used, thecorresponding measure for each clip may be determined by combining themeasures for the frames of that clip.

In step 430 the clips are ranked based on the extent to which humanfaces are represented in each clip. In some implementations, this may bebased on some combination of the average face area in each frame of theclip for which that value was determined. In some implementations, thismay be a summation of the average face area in each frame, an average ofthe average face area in each frame across the clip, or some otheroperation. In some implementations, clips may be additionally rankedbased on having a number of consecutive shots having a high average facearea.

In step 440 clips are selected based on the ranking. In someimplementations, a predetermined number of clips are selected, forexample the top 5 or 10 clips. In some implementations the highestranking clips are selected. In some implementations, clips may beadditionally selected to avoid redundancy resulting from the overlap ofclips. As noted above, some shots may be included in multiple clips. Ifa continuous sequence of shots of a media presentation has a highaverage face area, each clip that includes that sequence of shots willbe highly ranked. Because it is desirable to have a diverse set of clipsfor curation, personalization, and/or presentation, it is desirable toavoid having the selected clips contain overlapping shots of the mediapresentation.

To address this issue, in some implementations, clips are selected basedon both rank and the proximity of shots between clips. In suchimplementations, clips are selected to ensure each of the selected clipsis non-overlapping and separated from any of the other selected clips.This is accomplished by ensuring that there is a minimum separation(e.g., some number of shots or duration of content) between each of theshots in a highly ranked clip and any shot within any other clip. Forexample, if the highest ranked clip includes shots 200-230, and theminimum separation is 30 shots, then none of the other selected clipsmay include any of shots 170-260. In some implementations the minimumseparation may vary considerably, e.g., as few as 10 or as many as 50shots. It should be noted that this may result in selecting clips thatare not otherwise highly ranked relative to the highest ranked clips inthe entire set of clips. However, this also results in the selectedclips having little or no overlap; a desirable result in that a morediverse set of clips better supports subsequent personalization.

The result of FIG. 4 is a set of clips featuring close-up views ofcharacters that likely correspond to compelling scenes. Returning toFIG. 3 , the selected clips are further reviewed, e.g., for qualitycontrol and metadata tagging. Clips selected in accordance with theprocess of FIG. 4 may be tagged as character-themed clips. In step 340one or more of the selected clips is provided to a client device forplayback based on preferences associated with the client device. Asdiscussed above, a personalization engine may use a machine learningmodel to select clips, including clips selected in accordance with FIGS.4 and 5 , to provide to a client device.

FIG. 5 presents a flow diagram illustrating a particular implementationin which clips are selected based on the loudness of the correspondingaudio. Louder clips often correspond with “high intensity” scenes thatmay be engaging for a viewer. As the loudness of a media presentationmay vary between media presentations, in some implementations, clips areselected based on a threshold loudness value that is determined for andunique to each media presentation. This is advantageous over relying ona universal loudness threshold, which may not work for all mediapresentations due to the differences in corresponding audio.

In step 510 clips of a media presentation are identified. In someimplementations, the clips are identified based on shot boundaries asdiscussed in reference to FIG. 3 . In some implementations, theidentified clips are the same clips identified in step 410 of FIG. 4 ,such that the same set of clips is used for identifying and selectingcharacter-themed and loudness-based clips.

In step 520 the loudness of the audio associated with each clip isdetermined. Loudness may be determined based on the loudness values foreach of multiple, consecutive portions of a clip, e.g., each second oreach shot. Loudness may be represented as a 1-dimensional loudnesssignal of the audio component of the media presentation with respect totime, e.g., Loudness Full Units Scale or Root Mean Square. In someimplementations the loudness signal may be filtered to smooth the signalusing, for example a median filter.

In step 530 a loudness threshold value is determined. In someimplementations the threshold value is based on the maximum value of theloudness signal (which may be a filtered signal) and a desired number ofclips to be selected. For example, if 10 clips are to be selected basedon loudness, the threshold value may be adjusted to ensure that at least10 clips qualify. The threshold value may be set relative to the maximumloudness value so as to appropriately scale the threshold value relativeto the overall loudness of the particular media presentation. In someimplementations, the threshold value may be set to a proportion orpercentage of the maximum value, e.g., 95%, 90%, 85%, etc.

In step 540 clips are selected based on the loudness of each clip andthe loudness threshold. In some implementations, the clips may be rankedbased on the total loudness of each clip and selected according to theranking. In other implementations, clips may be selected based on aminimum consecutive number of shots or time within each clip exceeding athreshold value of loudness. For example, a clip might be selected onlyif it has at least 2 to 5 consecutive shots for which the correspondingmeasures of loudness exceed the threshold value.

In step 550 the number of selected clips is checked to determine if aminimum number of clips has been selected. If the number of selectedclips is less than the minimum number, steps 530-550 are repeated afteradjusting the loudness threshold value. For example, if ten clips aredesired to be selected having at least 5 consecutive shots above theloudness threshold value, the threshold value may be initially set to95% of the maximum loudness value. If, upon analyzing the clips, it isdetermined that only 3 clips have 5 consecutive shots above the loudnessthreshold value, the loudness threshold value may be decreased and theclips re-analyzed. In some implementations, the loudness threshold valueis decreased according to a set value, for example 5% of the maximumloudness, such that the next loudness threshold value would be 90%. Theclips may then be re-analyzed to determine if at least ten clips have atleast 5 consecutive shots above the new loudness threshold value, withthe adjustment repeated as necessary to select the desired number ofclips. The desired number of clips may vary from as few as 2 to as manyas 20 clips.

In addition, or as an alternative, the number of consecutive shots forwhich the loudness of a qualifying clip exceeds the threshold may alsobe varied to achieve the desired number of clips. For example, ifdropping the threshold is not sufficient to reach the desired number ofclips, the required number of consecutive shots might be reduced (e.g.,from 5 to 4).

In some implementations, clips may also be selected based on proximityto each other, similar to step 440 of FIG. 4 . For example, each of theten clips may be required to satisfy the additional constraint ofensuring that there is a minimum separation (e.g., some number of shotsor duration of content) between each of the shots in a particular clipand each of the shots in any other selected clip. In suchimplementations, the loudness threshold value or number of consecutiveshots may have to be further lowered to facilitate meeting thisadditional constraint.

The result of FIG. 5 is a set of clips having loudness profiles likelycorresponding to compelling scenes. Returning to FIG. 3 , the selectedclips are further reviewed, e.g., for quality control and metadatatagging. Clips selected in accordance with the process of FIG. 5 may betagged as loudness-based clips. In step 340 one or more of the selectedclips is provided to a client device for playback based on preferencesassociated with the client device. As discussed above, a personalizationengine may use a machine learning model to select clips, including clipsselected in accordance with FIG. 5 , to provide to a client device.

FIG. 6 presents a flow diagram illustrating another particularimplementation in which clips are identified based on the loudness ofthe corresponding audio. The process of FIG. 6 may be used in additionto or as an alternative to the process of FIG. 5 to identify clips thatare compelling based on loudness. FIG. 6 relates to FIG. 3 as analternative to steps 310 and 320. That is, rather than identifyingcandidate clips and then selecting compelling clips from among thecandidate clips, the implementation illustrated in FIG. 6 identifies asa compelling clip any sufficiently long sequence of shots for which theloudness is sufficiently high.

In step 610 shots of a media presentation are identified. In someimplementations, the shots are identified based on shot boundaries asdiscussed in reference to FIG. 3 and step 300. In some implementations,the identified shots may have shot boundaries that are similar to theshot boundaries of clips identified in step 410 of FIG. 4 or 510 of FIG.5 .

In step 620 the loudness of the audio associated with each shot isdetermined. Loudness may be determined based on the loudness values foreach of multiple, consecutive portions of a clip, e.g., each second.Loudness may be represented as a 1-dimensional loudness signal of theaudio component of the media presentation with respect to time, e.g.,Loudness Full Units Scale or Root Mean Square. In some implementationsthe loudness signal may be filtered to smooth the signal using, forexample a median filter.

In step 630 a loudness threshold value is determined. In someimplementations the threshold value is based on the maximum value of theloudness signal (which may be a filtered signal) and a desired number ofclips to be selected. In some implementations step 630 may be performedin the same manner as step 530 as describe above.

In step 640 clips are identified based on the loudness of some number ofconsecutive shots, the loudness threshold, and a desired clip length.The loudness of each shot is compared against the loudness threshold,and if the loudness of the shot exceeds the loudness threshold, thelength of the current sequence of consecutive shots exceeding thethreshold is compared to a desired clip length. If the length of thesequence of shots exceeds or is sufficiently close to the desired cliplength, the sequence of shots is identified as a compelling clip.Alternatively, if the sequence of shots is not long enough, another shotmay be compared to the loudness threshold and added to the sequence ofshots until either a shot is encountered that fails to have a loudnessabove the loudness threshold, or the length of the sequence of shotsexceeds or is sufficiently close to (e.g., within one shot duration of)the desired clip length.

If the next shot does not have a loudness exceeding the loudnessthreshold, the sequence of shots is not identified as a clip, and a newsequence may be analyzed beginning with the next shot that has aloudness that exceeds the loudness threshold. In this manner, a clip isidentified in which each shot in the clip has a loudness above theloudness threshold. In some implementations, once a compelling clip isidentified, the shot immediately following the clip is analyzed todetermine if the loudness exceeds the threshold loudness, and may beused to start a new sequence of shots. In other implementations, anumber of shots may be skipped before starting a new sequence, e.g.,between about 30 to 50 shots. This may be desirable to reduce overlapand ensure identified clips are from diverse parts of a mediapresentation. This process may repeat until the entire mediapresentation has been analyzed to identify clips that only include shotsthat have a loudness exceeding the loudness threshold and the minimumdesired length.

In step 650 the number of identified clips is checked to determine if aminimum number of clips has been identified. If the number of identifiedclips is less than the minimum number, steps 630-650 are repeated afteradjusting the loudness threshold value. For example, if ten clips aredesired to be selected all having consecutive shots above the loudnessthreshold value, the threshold value may be initially set to 95% of themaximum loudness value. If, upon analyzing the clips using this initialthreshold value, it is determined that only 3 clips were identified, theloudness threshold value may be decreased and the shots re-analyzed withthe lower threshold. In some implementations, the loudness thresholdvalue is decreased according to a set value, for example 5% of themaximum loudness, such that the next loudness threshold value would be90%. The shots may then be re-analyzed to determine if at least tenclips having consecutive shots above the new loudness threshold valuecan be identified, with the adjustment repeated as necessary to selectthe desired number of clips. The desired number of clips may vary, e.g.,from as few as 2 to as many as 20 clips.

In addition, or as an alternative, the length of the desired clip may beadjusted. For example, if dropping the threshold is not sufficient toreach the desired number of clips, the required length of the clip maybe reduced (e.g., from 45 seconds to 30 seconds).

Implementations are also contemplated in which not every shot in asequence must exceed the threshold in order for the sequence to beidentified as a clip. For example, if a shot is encountered in a currentsequence that does not exceed the threshold but the next shot does, theprocess may continue without beginning a new sequence. Other variationson this theme that fall within the scope of this disclosure will beunderstood by those of skill in the art.

The result of the implementation illustrated in FIG. 6 is a set of clipshaving loudness profiles likely corresponding to compelling scenes.Returning to FIG. 3 , the identified clips are further reviewed, e.g.,for quality control and metadata tagging. Clips selected in accordancewith the process of FIG. 6 may be tagged as loudness-based clips. Instep 340 one or more of the selected clips is provided to a clientdevice for playback based on preferences associated with the clientdevice. As discussed above, a personalization engine may use a machinelearning model to select clips, including clips selected in accordancewith FIG. 6 , to provide to a client device.

While the subject matter of this application has been particularly shownand described with reference to specific implementations thereof, itwill be understood by those skilled in the art that changes in the formand details of the disclosed implementations may be made withoutdeparting from the spirit or scope of the invention. Examples of some ofthese implementations are illustrated in the accompanying drawings, andspecific details are set forth in order to provide a thoroughunderstanding thereof. It should be noted that implementations may bepracticed without some or all of these specific details. In addition,well known features may not have been described in detail to promoteclarity. Finally, although various advantages have been discussed hereinwith reference to various implementations, it will be understood thatthe scope of the invention should not be limited by reference to suchadvantages. Rather, the scope of the invention should be determined withreference to the appended claims.

What is claimed is:
 1. A method, comprising: identifying a plurality ofshot boundaries of a media presentation, each shot boundarycorresponding to a visual transition in the media presentation, whereinthe media presentation includes a plurality of shots, each shot beingdefined by a corresponding pair of the shot boundaries; identifying afirst plurality of clips based on the shot boundaries, each clipincluding a contiguous range of content of the media presentationbeginning and ending at corresponding shot boundaries; determining afirst score for each clip of the first plurality of clips, wherein thefirst score represents an extent to which human faces are represented inthe clip or a measure of loudness of audio content associated with theclip; selecting a subset of the first plurality of clips based on thefirst scores; and providing one or more clips from the subset of thefirst plurality of clips to a first client device.
 2. The method ofclaim 1, wherein the first score for each clip of the first plurality ofclips is based on an area occupied by human faces in one or more framesof the clip and a number of the human faces represented in the one ormore frames of the clip.
 3. The method of claim 1, wherein the firstscore represents a measure of loudness of audio content associated withthe clip.
 4. The method of claim 1, further comprising determining asecond score for each shot, the second score representing a measure ofloudness of audio content associated with the shot, wherein the firstscore is based on the second score for each shot in each clip.
 5. Themethod of claim 1, wherein identifying the first plurality of clipsincludes identifying a first clip for which the second score for each ofa consecutive number of the shots in the first clip exceeds a thresholdvalue.
 6. The method of claim 5, further comprising adjusting thethreshold value to achieve a predetermined number of clips in the firstplurality of clips.
 7. The method of claim 5, wherein determining thesecond score for each shot includes determining the measure of loudnessbased on a filtered signal representing the audio content associatedwith the shot.
 8. The method of claim 1, further comprising removing afirst clip from the subset of the first plurality of clips based ongraphic content within the first clip or a quality measure associatedwith the first clip.
 9. The method of claim 1, wherein selecting thesubset of the first plurality of clips includes excluding a first clipfrom inclusion in the subset of the first plurality of clips thatincludes overlapping content with a second clip included in the subsetof the first plurality of clips.
 10. The method of claim 9, wherein eachclip includes a sequence of the shots, and wherein at least one of theshots included in the first clip is within a minimum number of shots ofone of the shots included in the second clip, and wherein the secondclip has a higher first score than the first clip.
 11. A system,comprising memory and one or more processors configured for: identifyinga plurality of shot boundaries of a media presentation, each shotboundary corresponding to a visual transition in the media presentation,wherein the media presentation includes a plurality of shots, each shotbeing defined by a corresponding pair of the shot boundaries;identifying a first plurality of clips based on the shot boundaries,each clip including a contiguous range of content of the mediapresentation beginning and ending at corresponding shot boundaries;determining a first score for each clip of the first plurality of clips,wherein the first score represents an extent to which human faces arerepresented in the clip or a measure of loudness of audio contentassociated with the clip; selecting a subset of the first plurality ofclips based on the first scores; and providing one or more clips fromthe subset of the first plurality of clips to a first client device. 12.The system of claim 11, wherein the first score for each clip of thefirst plurality of clips is based on an area occupied by human faces inone or more frames of the clip and a number of the human facesrepresented in the one or more frames of the clip.
 13. The system ofclaim 11, wherein the first score represents a measure of loudness ofaudio content associated with the clip.
 14. The system of claim 11,further comprising determining a second score for each shot, the secondscore representing a measure of loudness of audio content associatedwith the shot, wherein the first score is based on the second score foreach shot in each clip.
 15. The system of claim 11, wherein identifyingthe first plurality of clips includes identifying a first clip for whichthe second score for each of a consecutive number of the shots in thefirst clip exceeds a threshold value.
 16. The system of claim 15,further comprising adjusting the threshold value to achieve apredetermined number of clips in the first plurality of clips.
 17. Thesystem of claim 15, wherein determining the second score for each shotincludes determining the measure of loudness based on a filtered signalrepresenting the audio content associated with the shot.
 18. The systemof claim 11, further comprising removing a first clip from the subset ofthe first plurality of clips based on graphic content within the firstclip or a quality measure associated with the first clip.
 19. The systemof claim 11, wherein selecting the subset of the first plurality ofclips includes excluding a first clip from inclusion in the subset ofthe first plurality of clips that includes overlapping content with asecond clip included in the subset of the first plurality of clips. 20.The system of claim 19, wherein each clip includes a sequence of theshots, and wherein at least one of the shots included in the first clipis within a minimum number of shots of one of the shots included in thesecond clip, and wherein the second clip has a higher first score thanthe first clip.